Automated API Generation via Machine Learning
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Solution Overview
Problem
The development and utilization of application programming interfaces (APIs) are resource-intensive and costly, often resulting in underutilization due to complexity and the need for manual generation, which inefficiently uses server processor capacity and memory.
Innovation Solution
A system and method that utilize natural language processing and machine learning to automate the generation of APIs, allowing for the identification of required entities, matching with existing APIs in a database, and iterative refinement based on user input, thereby reducing human intervention and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual generation of APIs by software developers is used, then API functionality and security can be ensured, but development costs increase and server processor capacity and memory are inefficiently utilized
Solution Approach 1:
The patent replaces the mechanical manual process of API development with an automated machine learning system. The ML model automatically generates APIs by processing natural language descriptions and generating code, eliminating the need for manual programming while maintaining security and functionality through built-in validation and template-based approaches.
Solution Approach 2:
The system enables self-service API generation where the automated ML-based system performs the entire API development process without requiring software developers. The system automatically interprets requirements, generates code, validates security protocols, and deploys APIs, making the process autonomous and efficient.
2Adaptability or versatility
If complex APIs are created to interact with multiple computer systems, then data communication capabilities are enhanced, but API complexity increases and development costs rise
Solution Approach 1:
The patent implements a universal API generation system that can create APIs for multiple different computer systems and communication protocols through a single ML model. The system learns from diverse training data and can generate APIs that interact with various systems, making the solution multi-functional and adaptable without increasing individual API complexity.
Solution Approach 2:
The system uses parameter-based configuration where the ML model adjusts API generation parameters based on the target system requirements. By changing parameters such as protocol type, data format, and integration requirements, the same underlying system can generate diverse APIs for different computer systems without increasing structural complexity.
3Reliability
If secure and fully-featured APIs are developed, then API functionality and reliability are improved, but development time and costs increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training the ML model on extensive API development data, security protocols, and best practices before actual API generation. The system pre-configures security templates, validation rules, and code structures, so that when generating a new API, all security and feature requirements are already embedded in the generation process, eliminating the need for time-consuming manual implementation.
Solution Approach 2:
The system uses copying by leveraging pre-existing API templates and code patterns from the training data. The ML model copies and adapts proven security implementations and feature structures from existing APIs, ensuring reliability and security are maintained while significantly reducing development time compared to creating everything from scratch.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining first user-generated input, the first user-generated input indicates a request for generating an (API, identifying a first group of entities from the request, and determining whether there is a match between the first group of entities and a second group of entities stored in an API database according to API metadata. Further embodiments include identifying an API from the API database resulting in an identified API, presenting the identified API to the user, obtaining second user-generated input, and adjusting the request according to the second user-generated. Additional embodiments can include identifying a third group of entities from the adjusted request, and determining whether there is a match between the third group of entities and a fourth group of entities stored in the API database according to the API metadata. Other embodiments are disclosed.